The following abbreviations will be used throughout the specification as follows:                “AD”—Alzheimer's disease        “MCI”—mild cognitive impairment        “GDS”—global deterioration scale        “MRglc”—glucose metabolism        “FDG”—18-fluoro-2-deoxyglucose 2-[18F]fluoro-2-deoxy-D-glucose        “PET”—positron-emission tomography        “MR”—magnetic resonance        “ROI”—region of interest        “SPM”—statistical parametric mapping        “VBA”—voxel-based analysis        “HipMask”—hippocampus mask        
Certain publicly-available references are noted in this document, as appropriate. All of the references listed or referenced herein are incorporated herein in their entireties.
Both postmortem as well as in vivo Magnetic Resonance (“MR”) imaging studies have identified the medial temporal lobe (“MTL”), and the hippocampus in particular, as one of the first sites of pathological involvement and early atrophy in Alzheimer's Disease (“AD”). The hippocampus is involved very early in the natural history of AD, and has been shown to be quite vulnerable to the pathology of the disease, namely Amyloid-beta deposition in extracellular plaques and vascular walls, accumulation of intracellular neurofibrillary tangles (“NFT”), synaptic reductions, neuronal loss, and volume loss (atrophy). Consequently, to facilitate the early diagnosis of AD, it may be useful to accurately assess the structural and functional integrity of the hippocampus.
Many MR studies describe reductions in the hippocampus volume relative to aged matched controls. The volume losses range between 25 and 50%, depending on disease severity. Furthermore, there is also clear evidence of hippocampal volume reductions among individuals with Mild Cognitive Impairment (“MCI”), a clinical group at high risk for AD. These hippocampal volume reductions have been found to be sufficiently reliable to identify the MCI patients who eventually convert to AD.
In contrast, the PET literature is not clear on the importance of the hippocampus assessment in AD. Significant reductions in hippocampal glucose metabolism (MRglc) in AD have been shown only by the few studies that have utilized MR for sampling the PET. These studies relied upon within-subject rigid body registration of the PET/MR scans and a traditional region-of-interest (ROI) method, as known to those skilled in the art and described herein. The ROI method requires MR scans to guide the PET sampling and time-consuming manual outlining of the hippocampus.
Another approach to image analysis is a surface projection method, based on simplifying three-dimensional data based on its radial projection on brain cortical surface. Unfortunately, current implementations typically preserve only the maximum metabolic activity along each projection ray, thus rendering hippocampal hypometabolism invisible to the human observer. Yet another approach can be the use of inter-subject image averaging, in which an individual three-dimensional dataset is morphed onto a PET template and each voxel is compared against a normative distribution of metabolic activity. Many PET studies utilize such fully automated analytic technique, enabling researchers to examine the whole brain at the single voxel level Several PET studies using this approach have been able to replicate previous ROI findings of hypometabolism within the temporo-parietal and posterior cingulate cortex in AD, along with the frontal cortex in advanced disease. However, few (if any) PET studies using voxel-based or surface projection methods report hippocampal metabolic abnormalities in MCI or AD as compared to controls.
Automated voxel-based methods typically rely on a series of pre-processing steps, such as spatial normalization and smoothing of scans, which attempt to put all the image volumes into the same spatial coordinate system and reduce intra-subject variability. Because of small size and variable position of the HIP within the brain, these procedures fail to identify hippocampal MRglc alterations in MCI and AD and that minimizing these sources of error could identify such alterations.
As yet another alternative, the distribution of FDG uptake, one tracer of brain glucose metabolism, may be evaluated by visual inspection of PET scans. Many studies have shown that reductions of brain glucose metabolism, as assessed with FDG-PET, are diagnostically useful for AD and possibly other neurodegenerative diseases. As with the prior discussion of PET studies, such studies rely on estimation of changes in cortical brain metabolism and have not reported data on the hippocampus.
Several methods can be used to estimate changes in brain metabolism. The most common one of these methods are visual qualitative ratings, MRI-guided ROI and automated voxel-based analysis techniques. As described above, ROI and voxel-based techniques are mainly used for research purposes in studies on selected groups of patients and controls, require intensive pre-processing labor, and rely on dedicated software. To be used in the routine clinical examination of dementia, a diagnostic tool has to be easy to use and operator-independent. The most commonly used technique to evaluate brain metabolism in the clinical practice is the visual inspection logic of FDG-PET scans. Conventionally, visual inspection of the PET in the AD diagnosis focused on the cortex, mainly parieto-temporal, posterior cingulate (PCC) and/or frontal regions. There is evidence that cortical PET ratings are sensitive discriminators of AD and useful in the differential diagnosis from other dementias. These results of cortical hypometabolism have been confirmed by ROI and VBA studies.
However, the usefulness of cortical hypometabolism in identifying patients with MCI is controversial. Some FDG-PET studies in MCI reported cortical metabolic reductions, while several others did not. On the other hand, studies using MRI based ROI sampling consistently show significant hypometabolism in the (MTL in MCI and AD. As such, the ROI FDG-PET data is consistent with post-mortem and in vivo structural MRI studies showing that the MTL an early site of pathological involvement and early volume loss (atrophy) in AD.
Similarly, current methods and apparatuses for anatomically validating MTL hypometabolism may be unreliable or imprecise, especially when a visual evaluation is employed. Generally, visual evaluations fall short of the “gold standard” or computerized ROI measurements of metabolism. Computerized ROI implementations, however, may be both time-consuming and relatively expensive when compared to visual evaluations, and thus may be less suitable for diagnostic purposes.
Additionally, prior FDG-PET studies show that the characteristic pattern of cortical hypometabolism observed in AD enables accurate AD identification in 90-100% of the cases. Moreover, there is evidence that a positive PET diagnosis for progressive neurodegenerative disorder predicts future cognitive deterioration with 84% accuracy, and significantly improves prediction of subsequent clinical course over the clinical working diagnosis. However, these studies compared AD patients to normal controls and did not evaluate the accuracy of PET ratings in the diagnosis of MCI or to normal subjects who may be at increased future risk for MCI or AD.
Similarly, single-photon emission computed tomography (SPECT) scans may be employed as relatively sensitive discriminators of AD, and have been useful in the differential diagnosis of AD from other dementias. Briefly, SPECT scans use measures of perfusion to estimate damage to the brain. However, like PET scans, SPECT scans have shown only a limited ability to examine a patient's hippocampus.
Accordingly, improved methods, systems and storage medium for sampling and analyzing brain tissue that overcome the shortcomings of the previous methods and systems are preferable.